Made byBobr AI

Building Fair Pay: A Guide to Compensation Benchmarking

Learn how organizations build accurate, defensible pay benchmarks using market data, medians, and job matching to ensure fair and competitive compensation.

#compensation-and-benefits#hr-strategy#pay-benchmarking#talent-retention#salary-survey#total-rewards
Watch
Pitch

Understanding Our Market Data

Building Confidence in Pay Benchmarking

How We Ensure Our Pay Data is Accurate, Current & Defensible

Reward & Pay Strategy

Made byBobr AI

What We Keep Hearing...

"I got a 30% pay rise when I left!"

"Company X pays way more for the same role"

"Our market data must be outdated"

"Someone told me the market has moved significantly"

1 person's experience ≠ market reality. Our data covers 600+ organisations and thousands of data points.

Reward & Pay Strategy

Made byBobr AI

Our Market Data Approach





Why We Benchmark

Market Competitiveness & Fairness

Consistency Across Grades & Geographies

Pay Transparency & Governance

1

Data Submission & Collection

2

Market Data Validation & Job Matching

3

Statistical Analysis & Adjustments

4

Governance & Quality Assurance

5

Final Pay Range Construction

Our Data Covers

600+
Organisations
All
Big 4 Firms
500+
Consulting Firms
30+
Legal Firms
Made byBobr AI

How We Benchmark Pay

Our 4-Step Approach to Fair, Accurate Pay Positioning

01

How Are Roles Matched?

Job Grouping + Pay Grade + Service Type + Geozone = Fair Like-for-Like Comparison

02

Job Matching & Data Validation

Match to survey codes → Validate sample size (min 5 orgs) → Adjust granularity if needed

03

How We Calculate Market Median

Raw median → Age forward → Blend across surveys = Smoothes=Single balanced reference point

04

Final Pay Construction

Median = Pay Range Midpoint → Leadership sets percentile targets based on affordability → Market-aligned & equitable

WE USE WTW,MCLAGAN,MERCER ETC. — THE WORLD'S LEADING INDEPENDENT SALARY SURVEY PROVIDERS.

Made byBobr AI

Why the Median Protects Against Noise

MEDIAN Someone leaves saying +30% hike

Median = the true middle of thousands of data points. Outliers don't move it.

Statistically Resilient

Median acts as a dampener. A few extreme outliers easily skew an average, but have near zero impact on a median. 

Aged Forward

Data is reliably aged forward annually, ensuring all numbers accurately reflect current, up-to-date pay timing.

Balanced Reference

Blended securely across multiple reputable surveys to provide a single, balanced, and defensible source of truth.

One employee's pay offer is 1 data point. Our median is built on thousands

Made byBobr AI

Busting the Myths

Addressing Common Misconceptions

❌ The Myth
✅ The Reality
Reward can check what Company X pays
Not legally possible — data is anonymised & aggregated across hundreds of firms
We can pick our own peer group
Peer groups are set by data providers — we pay for a custom study, not cherry-picked
Job titles should match directly
Titles vary widely — we use standardised job codes for accurate like-for-like matching
Market data shows live pay
Data is annual and aged forward — it reflects current market timing
Missing companies break the data
Medians rely on large datasets — 2-3 missing firms have zero meaningful impact
Made byBobr AI

The Risk of Ignoring Benchmarked Data

With Benchmarked Data

Objective, defensible pay decisions

Consistent pay across grades

Evidence-based reward strategy

Protection against pay drift

Leadership confidence in pay

Without Benchmarked Data

Pay decisions based on rumour & anecdote

Reactive, inconsistent outcomes

Increased pay volatility & bias

Loss of competitive market insight

Reduced leadership confidence

Anecdotal evidence creates noise. Benchmarked data creates strategy.

Made byBobr AI

From Data to Pay Range:Our Process

1

External Survey Data

WTW, Mercer, Radford, Vencon — 600+ orgs

2

Job Matching

Match EY roles to survey codes using Job Group + Grade + Service + Geozone

3

Market Median Calculated

50th percentile — aged forward, blended across surveys

4

Pay Range Tool

Median becomes the indicative midpoint. Width & final percentiles set by Reward based on SL affordability and agreed with SL leadership

5

Final Pay Range

Market-aligned, equitable, defensible pay structure

Every pay range starts with independently validated market data — not guesswork.

Made byBobr AI

Our Market Data: Robust, Current & Defensible

Independent & Validated

Data from WTW & Mercer across 600+ organisations — not internal estimates

Statistically Robust

Medians are resilient to outliers. Missing 2–3 firms has zero impact on the result

Annually Updated

Data aged forward each cycle to reflect current pay market timing

“We benchmark to the market — not to the loudest voice in the room.”

Made byBobr AI
Bobr AI

DESIGNER-MADE
PRESENTATION,
GENERATED FROM
YOUR PROMPT

Create your own professional slide deck with real images, data charts, and unique design in under a minute.

Generate For Free

Building Fair Pay: A Guide to Compensation Benchmarking

Learn how organizations build accurate, defensible pay benchmarks using market data, medians, and job matching to ensure fair and competitive compensation.

Understanding Our Market Data

Building Confidence in Pay Benchmarking

How We Ensure Our Pay Data is Accurate, Current & Defensible

Reward & Pay Strategy

What We Keep Hearing...

I got a 30% pay rise when I left!

Company X pays way more for the same role

Our market data must be outdated

Someone told me the market has moved significantly

1 person's experience ≠ market reality. Our data covers 600+ organisations and thousands of data points.

Reward & Pay Strategy

Our Market Data Approach<br/><br/><br/><br/><br/><br/>

Market Competitiveness & Fairness

Consistency Across Grades & Geographies<br/>

Pay Transparency & Governance

Data Submission & Collection

Market Data Validation & Job Matching

Statistical Analysis & Adjustments

Governance & Quality Assurance

Final Pay Range Construction

600+

Organisations

All

Big 4 Firms

500+

Consulting Firms

30+

Legal Firms

How We Benchmark Pay

Our 4-Step Approach to Fair, Accurate Pay Positioning

01

How Are Roles Matched?

Job Grouping + Pay Grade + Service Type + Geozone = Fair Like-for-Like Comparison

02

Job Matching & Data Validation

Match to survey codes → Validate sample size (min 5 orgs) → Adjust granularity if needed

03

How We Calculate Market Median

Raw median → Age forward → Blend across surveys = Smoothes=Single balanced reference point

04

Final Pay Construction

Median = Pay Range Midpoint → Leadership sets percentile targets based on affordability → Market-aligned & equitable

WE USE WTW,MCLAGAN,MERCER ETC. — THE WORLD'S LEADING INDEPENDENT SALARY SURVEY PROVIDERS.

Why the Median Protects Against Noise

Statistically Resilient

Median acts as a dampener. A few extreme outliers easily skew an average, but have near zero impact on a median. 

Aged Forward

Data is reliably aged forward annually, ensuring all numbers accurately reflect current, up-to-date pay timing.

Balanced Reference

Blended securely across multiple reputable surveys to provide a single, balanced, and defensible source of truth.

One employee's pay offer is 1 data point. Our median is built on thousands

Busting the Myths

Addressing Common Misconceptions

❌ The Myth

✅ The Reality

Reward can check what Company X pays

Not legally possible — data is anonymised & aggregated across hundreds of firms

We can pick our own peer group

Peer groups are set by data providers — we pay for a custom study, not cherry-picked

Job titles should match directly

Titles vary widely — we use standardised job codes for accurate like-for-like matching

Market data shows live pay

Data is annual and aged forward — it reflects current market timing

Missing companies break the data

Medians rely on large datasets — 2-3 missing firms have zero meaningful impact

The Risk of Ignoring Benchmarked Data

With Benchmarked Data

Without Benchmarked Data

Objective, defensible pay decisions

Consistent pay across grades

Evidence-based reward strategy

Protection against pay drift

Leadership confidence in pay

Pay decisions based on rumour & anecdote

Reactive, inconsistent outcomes

Increased pay volatility & bias

Loss of competitive market insight

Reduced leadership confidence

Anecdotal evidence creates noise. Benchmarked data creates strategy.

From Data to Pay Range:

Our Process

External Survey Data

WTW, Mercer, Radford, Vencon — 600+ orgs

Job Matching

Match EY roles to survey codes using Job Group + Grade + Service + Geozone

Market Median Calculated

50th percentile — aged forward, blended across surveys

Pay Range Tool

Median becomes the indicative midpoint. Width & final percentiles set by Reward based on SL affordability and agreed with SL leadership

Final Pay Range

Market-aligned, equitable, defensible pay structure

Every pay range starts with independently validated market data — not guesswork.

Our Market Data: Robust, Current & Defensible

Independent & Validated

Data from WTW & Mercer across 600+ organisations — not internal estimates

Statistically Robust

Medians are resilient to outliers. Missing 2–3 firms has zero impact on the result

Annually Updated

Data aged forward each cycle to reflect current pay market timing

We benchmark to the market — not to the loudest voice in the room.